------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/bschaf03/Dropbox/Local and National Ideology Survey/Analysis/Replication Fil
> es/ces_replication.log
  log type:  text
 opened on:  20 Jun 2024, 12:16:08

. 
. 
. * Import data
. 
. use CCES21_TUF_OUTPUT.dta, clear

. 
. * Recode local items to that don't know is missing and no is 0 and to adjust for reverse coded
>  items
. recode TUF301a 1=0 2=1 3=., gen(taxbreaks_businesses)
(988 differences between TUF301a and taxbreaks_businesses)

. recode TUF301b 1=0 2=1 3=., gen(increase_parking)
(990 differences between TUF301b and increase_parking)

. recode TUF301c 2=0 3=., gen(affordable_housing)
(295 differences between TUF301c and affordable_housing)

. recode TUF301d 2=0 3=., gen(condemn_blight)
(446 differences between TUF301d and condemn_blight)

. recode TUF301e 2=0 3=., gen(apt_buildings)
(535 differences between TUF301e and apt_buildings)

. recode TUF301f 2=0 3=., gen(rent_controls)
(373 differences between TUF301f and rent_controls)

. 
. * Comparing separately scaled national and local policy indexes
. 
. irt 2pl taxbreaks_businesses-rent_controls, intm(mc)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -2744.4876  
Iteration 1:   log likelihood = -2740.1845  
Iteration 2:   log likelihood = -2740.1815  
Iteration 3:   log likelihood = -2740.1815  

Fitting full model:

Iteration 0:   log likelihood = -2632.8804  
Iteration 1:   log likelihood = -2589.8439  
Iteration 2:   log likelihood = -2559.4714  
Iteration 3:   log likelihood = -2554.6007  
Iteration 4:   log likelihood =  -2554.242  
Iteration 5:   log likelihood = -2553.9866  
Iteration 6:   log likelihood = -2553.9079  
Iteration 7:   log likelihood = -2553.9683  
Iteration 8:   log likelihood = -2553.9403  
Iteration 9:   log likelihood =  -2553.952  
Iteration 10:  log likelihood = -2553.9474  
Iteration 11:  log likelihood = -2553.9492  
Iteration 12:  log likelihood = -2553.9485  
Iteration 13:  log likelihood = -2553.9488  

Two-parameter logistic model                               Number of obs = 957
Log likelihood = -2553.9488
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
taxbreaks_~s |
     Discrim |   .4740115   .1211878     3.91   0.000     .2364877    .7115353
        Diff |   2.872233   .6926727     4.15   0.000      1.51462    4.229847
-------------+----------------------------------------------------------------
increase_p~g |
     Discrim |   .1920519   .1048197     1.83   0.067    -.0133909    .3974948
        Diff |   2.967585   1.616909     1.84   0.066    -.2014983    6.136669
-------------+----------------------------------------------------------------
affordable~g |
     Discrim |  -5.809502   1.238504    -4.69   0.000    -8.236925   -3.382079
        Diff |   .9182659   .0557907    16.46   0.000     .8089182    1.027614
-------------+----------------------------------------------------------------
condemn_bl~t |
     Discrim |  -.8550349     .13376    -6.39   0.000      -1.1172     -.59287
        Diff |   1.451245   .2015095     7.20   0.000     1.056293    1.846196
-------------+----------------------------------------------------------------
apt_buildi~s |
     Discrim |   -1.42913   .1761038    -8.12   0.000    -1.774287   -1.083973
        Diff |   .3310569   .0723673     4.57   0.000     .1892197    .4728942
-------------+----------------------------------------------------------------
rent_contr~s |
     Discrim |  -1.999436   .2494578    -8.02   0.000    -2.488364   -1.510508
        Diff |    .786239   .0757404    10.38   0.000     .6377905    .9346875
------------------------------------------------------------------------------

. predict localscale, latent
(option ebmeans assumed)
(using 7 quadrature points)

. irtgraph iif

. 
. * Recode national items and scale
. recode CC21_320a-CC21_321f CC21_322a-CC21_324d CC21_350a-CC21_350i CC21_355a-CC21_355e (2=0)
(368 changes made to CC21_320a)
(564 changes made to CC21_320b)
(593 changes made to CC21_320c)
(226 changes made to CC21_320d)
(515 changes made to CC21_320e)
(398 changes made to CC21_321a)
(641 changes made to CC21_321b)
(506 changes made to CC21_321c)
(667 changes made to CC21_321d)
(226 changes made to CC21_321e)
(111 changes made to CC21_321f)
(340 changes made to CC21_322a)
(334 changes made to CC21_322b)
(547 changes made to CC21_322c)
(560 changes made to CC21_322d)
(282 changes made to CC21_322e)
(389 changes made to CC21_323a)
(585 changes made to CC21_323b)
(630 changes made to CC21_323c)
(443 changes made to CC21_323d)
(803 changes made to CC21_323e)
(363 changes made to CC21_323f)
(349 changes made to CC21_324a)
(374 changes made to CC21_324b)
(393 changes made to CC21_324c)
(370 changes made to CC21_324d)
(371 changes made to CC21_350a)
(395 changes made to CC21_350b)
(163 changes made to CC21_350c)
(287 changes made to CC21_350d)
(278 changes made to CC21_350e)
(278 changes made to CC21_350f)
(113 changes made to CC21_350g)
(378 changes made to CC21_350h)
(217 changes made to CC21_350i)
(362 changes made to CC21_355a)
(339 changes made to CC21_355b)
(313 changes made to CC21_355c)
(369 changes made to CC21_355d)
(298 changes made to CC21_355e)

. 
. irt 2pl CC21_320a-CC21_321f CC21_322a-CC21_324d CC21_350a-CC21_350i CC21_355a-CC21_355e, intm(
> mc)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -24831.653  
Iteration 1:   log likelihood =  -24790.81  
Iteration 2:   log likelihood = -24790.779  
Iteration 3:   log likelihood = -24790.779  

Fitting full model:

Iteration 0:   log likelihood = -19540.793  
Iteration 1:   log likelihood = -18700.998  
Iteration 2:   log likelihood = -18446.658  
Iteration 3:   log likelihood = -18405.017  
Iteration 4:   log likelihood = -18404.192  
Iteration 5:   log likelihood = -18404.188  
Iteration 6:   log likelihood = -18404.188  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -18404.188
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
CC21_320a    |
     Discrim |   2.665853    .197635    13.49   0.000     2.278496    3.053211
        Diff |   -.419777   .0483018    -8.69   0.000    -.5144468   -.3251071
-------------+----------------------------------------------------------------
CC21_320b    |
     Discrim |  -1.956455   .1438699   -13.60   0.000    -2.238435   -1.674476
        Diff |  -.2316217   .0525799    -4.41   0.000    -.3346764   -.1285669
-------------+----------------------------------------------------------------
CC21_320c    |
     Discrim |   1.175523   .0999282    11.76   0.000     .9796676    1.371379
        Diff |   .3974436   .0734034     5.41   0.000     .2535755    .5413117
-------------+----------------------------------------------------------------
CC21_320d    |
     Discrim |   2.418852   .1917741    12.61   0.000     2.042981    2.794722
        Diff |   -.938398   .0601762   -15.59   0.000    -1.056341   -.8204549
-------------+----------------------------------------------------------------
CC21_320e    |
     Discrim |  -.7688413   .0810235    -9.49   0.000    -.9276444   -.6100382
        Diff |  -.0953024   .0931599    -1.02   0.306    -.2778924    .0872877
-------------+----------------------------------------------------------------
CC21_321a    |
     Discrim |   1.892455   .1405421    13.47   0.000     1.616998    2.167913
        Diff |  -.3648091   .0546358    -6.68   0.000    -.4718933    -.257725
-------------+----------------------------------------------------------------
CC21_321b    |
     Discrim |  -1.499329   .1178548   -12.72   0.000     -1.73032   -1.268338
        Diff |  -.5573096   .0651223    -8.56   0.000     -.684947   -.4296721
-------------+----------------------------------------------------------------
CC21_321c    |
     Discrim |  -1.156109   .0972292   -11.89   0.000    -1.346675   -.9655433
        Diff |  -.0379015    .069151    -0.55   0.584    -.1734351     .097632
-------------+----------------------------------------------------------------
CC21_321d    |
     Discrim |   1.022109   .0949109    10.77   0.000     .8360869    1.208131
        Diff |   .8166645   .0962857     8.48   0.000     .6279479    1.005381
-------------+----------------------------------------------------------------
CC21_321e    |
     Discrim |   1.496366   .1293184    11.57   0.000     1.242907    1.749826
        Diff |  -1.147512   .0869762   -13.19   0.000    -1.317982   -.9770416
-------------+----------------------------------------------------------------
CC21_321f    |
     Discrim |   1.320052   .1480983     8.91   0.000     1.029784    1.610319
        Diff |  -2.010565   .1695882   -11.86   0.000    -2.342952   -1.678178
-------------+----------------------------------------------------------------
CC21_322a    |
     Discrim |   2.111367   .1572123    13.43   0.000     1.803237    2.419498
        Diff |  -.5478103   .0546258   -10.03   0.000    -.6548749   -.4407456
-------------+----------------------------------------------------------------
CC21_322b    |
     Discrim |  -1.496715    .119551   -12.52   0.000    -1.731031     -1.2624
        Diff |   .6359303   .0691224     9.20   0.000     .5004529    .7714076
-------------+----------------------------------------------------------------
CC21_322c    |
     Discrim |  -1.406786   .1101739   -12.77   0.000    -1.622723   -1.190849
        Diff |  -.1979277   .0619335    -3.20   0.001    -.3193151   -.0765403
-------------+----------------------------------------------------------------
CC21_322d    |
     Discrim |  -2.864836    .212244   -13.50   0.000    -3.280826   -2.448845
        Diff |  -.1966783   .0454295    -4.33   0.000    -.2857185   -.1076382
-------------+----------------------------------------------------------------
CC21_322e    |
     Discrim |   2.398503   .1819492    13.18   0.000     2.041889    2.755117
        Diff |  -.7263825   .0551071   -13.18   0.000    -.8343905   -.6183745
-------------+----------------------------------------------------------------
CC21_323a    |
     Discrim |   2.231025   .1634126    13.65   0.000     1.910742    2.551308
        Diff |  -.3736882   .0509694    -7.33   0.000    -.4735864   -.2737901
-------------+----------------------------------------------------------------
CC21_323b    |
     Discrim |  -.8134061   .0836774    -9.72   0.000    -.9774108   -.6494014
        Diff |  -.4902787    .097534    -5.03   0.000    -.6814418   -.2991157
-------------+----------------------------------------------------------------
CC21_323c    |
     Discrim |  -.8611023   .0869413    -9.90   0.000    -1.031504   -.6907004
        Diff |  -.7231882   .1029718    -7.02   0.000    -.9250093   -.5213671
-------------+----------------------------------------------------------------
CC21_323d    |
     Discrim |  -1.392819   .1095045   -12.72   0.000    -1.607443   -1.178194
        Diff |   .2110087    .063178     3.34   0.001      .087182    .3348354
-------------+----------------------------------------------------------------
CC21_323e    |
     Discrim |  -.8233968   .1002663    -8.21   0.000    -1.019915   -.6268786
        Diff |   -1.93493   .2142722    -9.03   0.000    -2.354896   -1.514964
-------------+----------------------------------------------------------------
CC21_323f    |
     Discrim |   2.284285   .1677607    13.62   0.000      1.95548     2.61309
        Diff |  -.4560724   .0514692    -8.86   0.000    -.5569501   -.3551947
-------------+----------------------------------------------------------------
CC21_324a    |
     Discrim |    3.91789   .3122067    12.55   0.000     3.305976    4.529804
        Diff |  -.4399544   .0436382   -10.08   0.000    -.5254837   -.3544251
-------------+----------------------------------------------------------------
CC21_324b    |
     Discrim |   2.847878   .2119489    13.44   0.000     2.432466     3.26329
        Diff |  -.3940816   .0469811    -8.39   0.000    -.4861629   -.3020003
-------------+----------------------------------------------------------------
CC21_324c    |
     Discrim |   3.359338   .2573622    13.05   0.000     2.854918    3.863759
        Diff |  -.3237739   .0442019    -7.32   0.000    -.4104081   -.2371398
-------------+----------------------------------------------------------------
CC21_324d    |
     Discrim |   1.653727   .1265102    13.07   0.000     1.405771    1.901682
        Diff |  -.4901311   .0603564    -8.12   0.000    -.6084274   -.3718347
-------------+----------------------------------------------------------------
CC21_350a    |
     Discrim |   3.423144   .2623556    13.05   0.000     2.908936    3.937352
        Diff |  -.3863994    .044509    -8.68   0.000    -.4736354   -.2991634
-------------+----------------------------------------------------------------
CC21_350b    |
     Discrim |   2.476007   .1820609    13.60   0.000     2.119174    2.832839
        Diff |   -.343028   .0487303    -7.04   0.000    -.4385377   -.2475184
-------------+----------------------------------------------------------------
CC21_350c    |
     Discrim |   2.050614   .1796411    11.42   0.000     1.698524    2.402704
        Diff |   -1.29217   .0793223   -16.29   0.000    -1.447638   -1.136701
-------------+----------------------------------------------------------------
CC21_350d    |
     Discrim |   1.511739   .1231548    12.28   0.000      1.27036    1.753118
        Diff |  -.8559352   .0736846   -11.62   0.000    -1.000354   -.7115161
-------------+----------------------------------------------------------------
CC21_350e    |
     Discrim |   2.322262   .1768246    13.13   0.000     1.975692    2.668832
        Diff |  -.7486807   .0563582   -13.28   0.000    -.8591407   -.6382206
-------------+----------------------------------------------------------------
CC21_350f    |
     Discrim |   2.407352   .1829575    13.16   0.000     2.048762    2.765943
        Diff |  -.7400847    .055355   -13.37   0.000    -.8485785   -.6315909
-------------+----------------------------------------------------------------
CC21_350g    |
     Discrim |   1.542474   .1619781     9.52   0.000     1.225003    1.859945
        Diff |  -1.819538   .1354629   -13.43   0.000    -2.085041   -1.554036
-------------+----------------------------------------------------------------
CC21_350h    |
     Discrim |   1.204151   .1019372    11.81   0.000     1.004358    1.403944
        Diff |  -.5423223   .0743561    -7.29   0.000    -.6880576    -.396587
-------------+----------------------------------------------------------------
CC21_350i    |
     Discrim |   1.342752   .1218412    11.02   0.000     1.103947    1.581556
        Diff |  -1.267501   .1004092   -12.62   0.000    -1.464299   -1.070702
-------------+----------------------------------------------------------------
CC21_355a    |
     Discrim |   4.713123   .4024672    11.71   0.000     3.924301    5.501944
        Diff |  -.3896517   .0415851    -9.37   0.000    -.4711571   -.3081464
-------------+----------------------------------------------------------------
CC21_355b    |
     Discrim |   4.422146   .3658537    12.09   0.000     3.705086    5.139206
        Diff |  -.4599328   .0427748   -10.75   0.000    -.5437699   -.3760957
-------------+----------------------------------------------------------------
CC21_355c    |
     Discrim |    1.07524   .0992637    10.83   0.000     .8806864    1.269793
        Diff |  -.9074009   .0953594    -9.52   0.000    -1.094302   -.7204999
-------------+----------------------------------------------------------------
CC21_355d    |
     Discrim |   2.739017   .2024408    13.53   0.000     2.342241    3.135794
        Diff |   -.413608   .0477522    -8.66   0.000    -.5072005   -.3200154
-------------+----------------------------------------------------------------
CC21_355e    |
     Discrim |   2.867813   .2168055    13.23   0.000     2.442882    3.292744
        Diff |  -.6360515   .0500948   -12.70   0.000    -.7342354   -.5378676
------------------------------------------------------------------------------

. predict natlscale, latent
(option ebmeans assumed)
(using 7 quadrature points)

. 
. * Reverse polarity to match polarity from Study 1
. gen natlscale_lc=natlscale*-1

. 
. recode ideo5 6=3
(78 changes made to ideo5)

. 
. pwcorr localscale natlscale ideo5, sig

             | locals~e natlsc~e    ideo5
-------------+---------------------------
  localscale |   1.0000 
             |
             |
   natlscale |  -0.5753   1.0000 
             |   0.0000
             |
       ideo5 |   0.3982  -0.7266   1.0000 
             |   0.0000   0.0000
             |

. 
. recode urbancity 3/5=3, gen(urbancity2)
(192 differences between urbancity and urbancity2)

. bysort urbancity2: pwcorr localscale natlscale, sig

------------------------------------------------------------------------------------------------
-> urbancity2 = 1

             | locals~e natlsc~e
-------------+------------------
  localscale |   1.0000 
             |
             |
   natlscale |  -0.5419   1.0000 
             |   0.0000
             |

------------------------------------------------------------------------------------------------
-> urbancity2 = 2

             | locals~e natlsc~e
-------------+------------------
  localscale |   1.0000 
             |
             |
   natlscale |  -0.5345   1.0000 
             |   0.0000
             |

------------------------------------------------------------------------------------------------
-> urbancity2 = 3

             | locals~e natlsc~e
-------------+------------------
  localscale |   1.0000 
             |
             |
   natlscale |  -0.6194   1.0000 
             |   0.0000
             |

. 
. 
. * Figure 5: Scatter plot of IRT issue scales (not in PAP)
. 
. twoway scatter localscale natlscale_lc, mc(black%50) aspect(1) xtitle("National issues scale")
>  ytitle("Local issues scale") || lfit localscale natlscale_lc, lc(red) lp(solid) legend(off) t
> ext(0 2.5 "r = 0.575")

. graph export Figure5.png, replace
file /Users/bschaf03/Dropbox/Local and National Ideology Survey/Analysis/Replication
    Files/Figure5.png saved as PNG format

. 
. * Figure 6: Local item correlations with national policy scale (Not in PAP)
. 
. local varlist  affordable_housing taxbreaks_businesses increase_parking condemn_blight apt_bui
> ldings rent_controls

. 
. 
. local nvars : word count `varlist' 

. 
. local N = `nvars' * (`nvars' - 1) / 2 

. 
. if `N' > _N set obs `N' 

. 
. gen x2 = "" 
(1,000 missing values generated)

. gen r2 = . 
(1,000 missing values generated)

. local k = 1 

. tokenize "`varlist'" 

. 
. forval i = 1/`nvars' { 
  2.     local J = `i' + 1 
  3.         quietly {
  4.             corr ``i'' natlscale 
  5.             replace x2 = "``i''" in `k' 
  6.             replace r2 = r(rho) in `k' 
  7.         }
  8.         local ++k 
  9.     }

. 
. gsort r2

. gen variable=_n if r2!=.
(994 missing values generated)

. 
. label define issues 1 "Oppose - Increase parking" 2 "Oppose - Tax breaks for businesses" 3 "Su
> pport - Condemn blighted property" 4 "Support - Allow apartment buildings in neighborhood" 5 "
> Support - Rent control" 6 "Support - Affordable housing"

. label values variable issues

. 
. twoway bar r2 variable if r2!=., hor ylabel(1(1)6, val) color(navy%70) ytitle(" ") xtitle("Cor
> relation with national policy scale") xlabel(-.2(.1).6) aspect(1) barw(.75)

. graph export Figure6.png, replace
file /Users/bschaf03/Dropbox/Local and National Ideology Survey/Analysis/Replication
    Files/Figure6.png saved as PNG format

. 
. * Robustness check for SI (Figure A4): Create a loop to randomly select 6 national policy item
> s at a time for scaling 
. * Do this 30 times
. forval j = 1/30{
  2. 
. local setvars CC21_320a CC21_320b CC21_320c CC21_320d CC21_320e CC21_321a CC21_321b CC21_321c 
> CC21_321d CC21_321e CC21_321f CC21_322a CC21_322b CC21_322c CC21_322d CC21_322e CC21_323a CC21
> _323b CC21_323c CC21_323d CC21_323e CC21_323f CC21_324a CC21_324b CC21_324c CC21_324d CC21_350
> a CC21_350b CC21_350c CC21_350d CC21_350e CC21_350f CC21_350g CC21_350h CC21_350i CC21_355a CC
> 21_355b CC21_355c CC21_355d CC21_355e 
  3. 
. local size : word count `setvars'
  4. 
. set seed 123`j'
  5. 
. 
. forval i = 1/6 {
  6.    local nextvar = word("`setvars'", runiformint(1, `size'))
  7.    di "newvar`i' will get the values of `nextvar'."
  8.    gen newvar`i' = `nextvar'
  9.    // Don't pick this var again
.    local setvars: list setvars - nextvar
 10.    local size = `size' - 1
 11. }
 12. irt 2pl newvar1 newvar2 newvar3 newvar4 newvar5 newvar6, intm(mc)
 13. predict natscale`j'
 14. drop newvar1 newvar2 newvar3 newvar4 newvar5 newvar6
 15. }
newvar1 will get the values of CC21_350g.
newvar2 will get the values of CC21_321c.
newvar3 will get the values of CC21_355d.
newvar4 will get the values of CC21_350e.
newvar5 will get the values of CC21_323f.
newvar6 will get the values of CC21_355e.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3566.8242  
Iteration 1:   log likelihood = -3559.5877  
Iteration 2:   log likelihood = -3559.5779  
Iteration 3:   log likelihood = -3559.5779  

Fitting full model:

Iteration 0:   log likelihood = -3345.4848  
Iteration 1:   log likelihood = -3059.2507  
Iteration 2:   log likelihood = -3024.8083  
Iteration 3:   log likelihood = -3021.9919  
Iteration 4:   log likelihood = -3021.9631  
Iteration 5:   log likelihood = -3021.9632  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3021.9632
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.630472    .198631     8.21   0.000     1.241163    2.019782
        Diff |  -1.777561    .140197   -12.68   0.000    -2.052342    -1.50278
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -1.038138    .108113    -9.60   0.000    -1.250036   -.8262409
        Diff |  -.0387377   .0746731    -0.52   0.604    -.1850944     .107619
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   2.886346   .3159143     9.14   0.000     2.267165    3.505527
        Diff |  -.4111731   .0477359    -8.61   0.000    -.5047337   -.3176125
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   2.345483   .2366106     9.91   0.000     1.881735    2.809232
        Diff |  -.7480938   .0582042   -12.85   0.000    -.8621719   -.6340157
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   1.884574   .1741813    10.82   0.000     1.543184    2.225963
        Diff |  -.4888746   .0576342    -8.48   0.000    -.6018355   -.3759136
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   2.922401   .3262971     8.96   0.000     2.282871    3.561932
        Diff |   -.636039   .0512509   -12.41   0.000     -.736489   -.5355891
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_350c.
newvar2 will get the values of CC21_350g.
newvar3 will get the values of CC21_323a.
newvar4 will get the values of CC21_350b.
newvar5 will get the values of CC21_322a.
newvar6 will get the values of CC21_320a.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3441.5787  
Iteration 1:   log likelihood = -3435.5098  
Iteration 2:   log likelihood = -3435.5022  
Iteration 3:   log likelihood = -3435.5022  

Fitting full model:

Iteration 0:   log likelihood = -3121.1305  
Iteration 1:   log likelihood = -2856.9592  
Iteration 2:   log likelihood = -2842.7925  
Iteration 3:   log likelihood = -2842.2962  
Iteration 4:   log likelihood = -2842.2931  
Iteration 5:   log likelihood = -2842.2931  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -2842.2931
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.972808   .2095749     9.41   0.000     1.562049    2.383567
        Diff |  -1.320151   .0889018   -14.85   0.000    -1.494396   -1.145907
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   1.394098   .1709411     8.16   0.000      1.05906    1.729136
        Diff |  -1.943275   .1679075   -11.57   0.000    -2.272368   -1.614182
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   1.872746   .1691865    11.07   0.000     1.541147    2.204346
        Diff |  -.3911227   .0566984    -6.90   0.000    -.5022494   -.2799959
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   2.956107   .3156247     9.37   0.000     2.337494     3.57472
        Diff |  -.3198669   .0471009    -6.79   0.000     -.412183   -.2275508
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   2.244573   .2110672    10.63   0.000     1.830889    2.658257
        Diff |  -.5308643   .0546643    -9.71   0.000    -.6380044   -.4237242
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   2.816105   .2906478     9.69   0.000     2.246446    3.385764
        Diff |  -.4072264    .048667    -8.37   0.000    -.5026119   -.3118409
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_324a.
newvar2 will get the values of CC21_322d.
newvar3 will get the values of CC21_322c.
newvar4 will get the values of CC21_323a.
newvar5 will get the values of CC21_320c.
newvar6 will get the values of CC21_350c.

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -3813.649  
Iteration 1:   log likelihood = -3810.1365  
Iteration 2:   log likelihood = -3810.1358  
Iteration 3:   log likelihood = -3810.1358  

Fitting full model:

Iteration 0:   log likelihood = -3441.0897  
Iteration 1:   log likelihood = -3235.5932  
Iteration 2:   log likelihood = -3222.8419  
Iteration 3:   log likelihood = -3222.0479  
Iteration 4:   log likelihood = -3222.0534  
Iteration 5:   log likelihood = -3222.0539  
Iteration 6:   log likelihood = -3222.0539  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3222.0539
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   2.728799   .2944284     9.27   0.000      2.15173    3.305868
        Diff |  -.4666362   .0504412    -9.25   0.000    -.5654992   -.3677732
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -3.607464    .502994    -7.17   0.000    -4.593314   -2.621614
        Diff |  -.1733538   .0438484    -3.95   0.000    -.2592951   -.0874126
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -1.666624   .1550478   -10.75   0.000    -1.970512   -1.362736
        Diff |  -.1735527   .0571956    -3.03   0.002    -.2856541   -.0614513
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   2.006998    .183284    10.95   0.000     1.647768    2.366228
        Diff |  -.3765648   .0547672    -6.88   0.000    -.4839065   -.2692231
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   1.189326   .1203998     9.88   0.000      .953347    1.425306
        Diff |   .4006871   .0739981     5.41   0.000     .2556535    .5457206
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   1.536047   .1695245     9.06   0.000     1.203785    1.868309
        Diff |  -1.482476   .1159555   -12.78   0.000    -1.709744   -1.255207
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_320d.
newvar2 will get the values of CC21_322b.
newvar3 will get the values of CC21_321b.
newvar4 will get the values of CC21_321e.
newvar5 will get the values of CC21_350i.
newvar6 will get the values of CC21_322a.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3529.4857  
Iteration 1:   log likelihood = -3522.7354  
Iteration 2:   log likelihood = -3522.7315  
Iteration 3:   log likelihood = -3522.7315  

Fitting full model:

Iteration 0:   log likelihood = -3300.9155  
Iteration 1:   log likelihood = -3142.2541  
Iteration 2:   log likelihood = -3134.5947  
Iteration 3:   log likelihood =  -3133.922  
Iteration 4:   log likelihood = -3133.9157  
Iteration 5:   log likelihood = -3133.9158  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3133.9158
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |    3.01959   .4097417     7.37   0.000     2.216511    3.822669
        Diff |   -.881265   .0589868   -14.94   0.000     -.996877    -.765653
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   -1.22161    .134718    -9.07   0.000    -1.485652   -.9575677
        Diff |   .7246115   .0870624     8.32   0.000     .5539723    .8952506
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -1.314323   .1338283    -9.82   0.000    -1.576621   -1.052024
        Diff |   -.592958    .074627    -7.95   0.000    -.7392243   -.4466918
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   1.847138   .1981116     9.32   0.000     1.458846    2.235429
        Diff |  -1.035377   .0786555   -13.16   0.000    -1.189539   -.8812154
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   1.146238   .1341013     8.55   0.000     .8834046    1.409072
        Diff |  -1.396749   .1351908   -10.33   0.000    -1.661719    -1.13178
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   2.310532   .2499939     9.24   0.000     1.820552    2.800511
        Diff |  -.5278558   .0541869    -9.74   0.000    -.6340602   -.4216515
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_320b.
newvar2 will get the values of CC21_323f.
newvar3 will get the values of CC21_350i.
newvar4 will get the values of CC21_355c.
newvar5 will get the values of CC21_320d.
newvar6 will get the values of CC21_321c.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3716.9171  
Iteration 1:   log likelihood = -3712.0978  
Iteration 2:   log likelihood =  -3712.095  
Iteration 3:   log likelihood =  -3712.095  

Fitting full model:

Iteration 0:   log likelihood = -3414.0908  
Iteration 1:   log likelihood = -3341.0934  
Iteration 2:   log likelihood = -3337.0207  
Iteration 3:   log likelihood = -3336.9112  
Iteration 4:   log likelihood = -3336.9114  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3336.9114
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.897441   .1953172     9.71   0.000     1.514626    2.280255
        Diff |   .2237488   .0543374     4.12   0.000     .1172494    .3302483
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -2.617373   .3236835    -8.09   0.000    -3.251781   -1.982965
        Diff |   .4299108   .0507472     8.47   0.000     .3304482    .5293734
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -1.205189   .1393076    -8.65   0.000    -1.478227   -.9321509
        Diff |   1.352277   .1274423    10.61   0.000     1.102494    1.602059
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |  -1.157931   .1256507    -9.22   0.000    -1.404202   -.9116599
        Diff |   .8608237    .095133     9.05   0.000     .6743664    1.047281
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |  -2.007017   .2259574    -8.88   0.000    -2.449885   -1.564149
        Diff |   .9986594   .0751035    13.30   0.000     .8514593     1.14586
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   1.022405   .1122307     9.11   0.000     .8024367    1.242373
        Diff |   .0324191   .0753957     0.43   0.667    -.1153538     .180192
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_323d.
newvar2 will get the values of CC21_324d.
newvar3 will get the values of CC21_350g.
newvar4 will get the values of CC21_350e.
newvar5 will get the values of CC21_321c.
newvar6 will get the values of CC21_323b.

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -3666.343  
Iteration 1:   log likelihood = -3661.0973  
Iteration 2:   log likelihood = -3661.0891  
Iteration 3:   log likelihood = -3661.0891  

Fitting full model:

Iteration 0:   log likelihood = -3458.9934  
Iteration 1:   log likelihood = -3399.2949  
Iteration 2:   log likelihood =   -3396.83  
Iteration 3:   log likelihood = -3396.6119  
Iteration 4:   log likelihood =   -3396.61  
Iteration 5:   log likelihood =   -3396.61  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3396.61
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.279678   .1556853     8.22   0.000     .9745399    1.584815
        Diff |  -.2288123   .0681974    -3.36   0.001    -.3624768   -.0951477
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -1.505113   .1705484    -8.83   0.000    -1.839382   -1.170845
        Diff |   .5086198    .067601     7.52   0.000     .3761243    .6411152
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -1.452065   .2191571    -6.63   0.000    -1.881605   -1.022525
        Diff |   1.884874   .1871067    10.07   0.000     1.518152    2.251596
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |  -2.305003   .3438162    -6.70   0.000    -2.978871   -1.631136
        Diff |   .7485073   .0650178    11.51   0.000     .6210747    .8759399
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   1.156462      .1338     8.64   0.000     .8942184    1.418705
        Diff |   .0333681   .0694648     0.48   0.631    -.1027803    .1695165
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   .9076492   .1206414     7.52   0.000     .6711963    1.144102
        Diff |    .449865   .0932982     4.82   0.000     .2670039     .632726
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_320e.
newvar2 will get the values of CC21_322b.
newvar3 will get the values of CC21_350i.
newvar4 will get the values of CC21_320c.
newvar5 will get the values of CC21_350a.
newvar6 will get the values of CC21_323e.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3688.6667  
Iteration 1:   log likelihood = -3684.2079  
Iteration 2:   log likelihood = -3684.2061  
Iteration 3:   log likelihood = -3684.2061  

Fitting full model:

Iteration 0:   log likelihood = -3511.7523  
Iteration 1:   log likelihood = -3471.4072  
Iteration 2:   log likelihood = -3453.2389  
Iteration 3:   log likelihood = -3449.0347  
Iteration 4:   log likelihood = -3448.8488  
Iteration 5:   log likelihood = -3448.7624  
Iteration 6:   log likelihood = -3448.7751  
Iteration 7:   log likelihood = -3448.7728  
Iteration 8:   log likelihood = -3448.7731  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3448.7731
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |    .603783    .092558     6.52   0.000     .4223727    .7851933
        Diff |   .1155884   .1156381     1.00   0.318    -.1110581    .3422348
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   1.133882   .1328694     8.53   0.000     .8734626    1.394301
        Diff |  -.7580052   .0949689    -7.98   0.000    -.9441409   -.5718695
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -1.187323   .1390793    -8.54   0.000    -1.459913   -.9147328
        Diff |    1.37358    .131341    10.46   0.000     1.116157    1.631004
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |  -1.188043   .1307143    -9.09   0.000    -1.444239   -.9318482
        Diff |  -.3992094   .0762096    -5.24   0.000    -.5485775   -.2498412
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |  -4.837931   .8205025    -5.90   0.000    -6.446086   -3.229776
        Diff |   .3668901   .0478166     7.67   0.000     .2731713    .4606089
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   .6437816   .1098464     5.86   0.000     .4284866    .8590765
        Diff |   2.375892   .3701658     6.42   0.000      1.65038    3.101403
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_322b.
newvar2 will get the values of CC21_321b.
newvar3 will get the values of CC21_321a.
newvar4 will get the values of CC21_323d.
newvar5 will get the values of CC21_350g.
newvar6 will get the values of CC21_355c.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3630.0264  
Iteration 1:   log likelihood = -3622.8741  
Iteration 2:   log likelihood = -3622.8653  
Iteration 3:   log likelihood = -3622.8653  

Fitting full model:

Iteration 0:   log likelihood = -3399.4948  
Iteration 1:   log likelihood =   -3301.38  
Iteration 2:   log likelihood =  -3295.573  
Iteration 3:   log likelihood = -3295.3164  
Iteration 4:   log likelihood = -3295.3159  
Iteration 5:   log likelihood =  -3295.316  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3295.316
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.487914    .166326     8.95   0.000     1.161921    1.813907
        Diff |  -.6470387   .0743477    -8.70   0.000    -.7927574   -.5013199
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   2.460366   .3034813     8.11   0.000     1.865554    3.055178
        Diff |   .4456777    .052778     8.44   0.000     .3422347    .5491208
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -2.294644   .2702489    -8.49   0.000    -2.824322   -1.764966
        Diff |     .32775   .0518957     6.32   0.000     .2260363    .4294636
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   1.276975   .1383407     9.23   0.000     1.005832    1.548118
        Diff |  -.2335518   .0675437    -3.46   0.001    -.3659351   -.1011686
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |  -1.182066   .1700736    -6.95   0.000    -1.515404   -.8487276
        Diff |   2.144887   .2301782     9.32   0.000     1.693746    2.596028
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   -.754259    .103007    -7.32   0.000     -.956149   -.5523691
        Diff |   1.170625    .163179     7.17   0.000     .8508004     1.49045
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_320b.
newvar2 will get the values of CC21_324c.
newvar3 will get the values of CC21_323a.
newvar4 will get the values of CC21_355c.
newvar5 will get the values of CC21_350a.
newvar6 will get the values of CC21_350c.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3753.7858  
Iteration 1:   log likelihood =  -3748.881  
Iteration 2:   log likelihood = -3748.8795  
Iteration 3:   log likelihood = -3748.8795  

Fitting full model:

Iteration 0:   log likelihood = -3256.0066  
Iteration 1:   log likelihood = -3126.8012  
Iteration 2:   log likelihood =  -3107.918  
Iteration 3:   log likelihood = -3107.2445  
Iteration 4:   log likelihood = -3107.2477  
Iteration 5:   log likelihood = -3107.2479  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3107.2479
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |    1.82986   .1621422    11.29   0.000     1.512067    2.147653
        Diff |   .2275966   .0552321     4.12   0.000     .1193436    .3358495
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -3.114238   .3370707    -9.24   0.000    -3.774884   -2.453592
        Diff |   .3194706   .0463415     6.89   0.000     .2286429    .4102983
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -1.915655    .170185   -11.26   0.000    -2.249211   -1.582098
        Diff |   .3860869   .0559096     6.91   0.000      .276506    .4956677
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |  -1.209928   .1193713   -10.14   0.000    -1.443891   -.9759644
        Diff |   .8410682   .0884826     9.51   0.000     .6676455    1.014491
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |  -3.289917   .3717197    -8.85   0.000    -4.018474   -2.561359
        Diff |   .3814753   .0462057     8.26   0.000     .2909137    .4720368
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |  -2.084944   .2234148    -9.33   0.000    -2.522829   -1.647059
        Diff |   1.291566   .0847767    15.23   0.000     1.125406    1.457725
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_355a.
newvar2 will get the values of CC21_355e.
newvar3 will get the values of CC21_350d.
newvar4 will get the values of CC21_322d.
newvar5 will get the values of CC21_324b.
newvar6 will get the values of CC21_321d.

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -3853.835  
Iteration 1:   log likelihood =  -3846.431  
Iteration 2:   log likelihood =  -3846.427  
Iteration 3:   log likelihood =  -3846.427  

Fitting full model:

Iteration 0:   log likelihood = -3424.4951  
Iteration 1:   log likelihood =  -3132.365  
Iteration 2:   log likelihood = -3118.7705  
Iteration 3:   log likelihood = -3115.6604  
Iteration 4:   log likelihood = -3115.6082  
Iteration 5:   log likelihood = -3115.6181  
Iteration 6:   log likelihood = -3115.6115  
Iteration 7:   log likelihood = -3115.6119  
Iteration 8:   log likelihood = -3115.6119  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3115.6119
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   5.997601   1.268966     4.73   0.000     3.510473    8.484728
        Diff |   -.370703   .0402227    -9.22   0.000    -.4495381   -.2918679
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   2.918888   .2788621    10.47   0.000     2.372329    3.465448
        Diff |  -.6230055   .0513383   -12.14   0.000    -.7236266   -.5223844
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   1.480875   .1364037    10.86   0.000     1.213529    1.748221
        Diff |  -.8641006   .0780984   -11.06   0.000    -1.017171   -.7110305
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |  -2.185775   .1898467   -11.51   0.000    -2.557868   -1.813683
        Diff |  -.2014821   .0510268    -3.95   0.000    -.3014929   -.1014714
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   2.616352   .2387416    10.96   0.000     2.148427    3.084277
        Diff |  -.3930183   .0495536    -7.93   0.000    -.4901416    -.295895
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   1.137705   .1184459     9.61   0.000     .9055551    1.369854
        Diff |   .7545032   .0909368     8.30   0.000     .5762704    .9327361
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_355d.
newvar2 will get the values of CC21_350c.
newvar3 will get the values of CC21_323e.
newvar4 will get the values of CC21_321a.
newvar5 will get the values of CC21_324b.
newvar6 will get the values of CC21_320c.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3612.3842  
Iteration 1:   log likelihood = -3608.2394  
Iteration 2:   log likelihood = -3608.2386  
Iteration 3:   log likelihood = -3608.2386  

Fitting full model:

Iteration 0:   log likelihood = -3291.7471  
Iteration 1:   log likelihood = -3148.6904  
Iteration 2:   log likelihood = -3140.2232  
Iteration 3:   log likelihood = -3139.8961  
Iteration 4:   log likelihood = -3139.8963  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3139.8963
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   2.432481   .2553026     9.53   0.000     1.932097    2.932864
        Diff |  -.4106218   .0516826    -7.95   0.000    -.5119178   -.3093258
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   1.876097   .2059946     9.11   0.000     1.472354    2.279839
        Diff |  -1.342183   .0949896   -14.13   0.000    -1.528359   -1.156007
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -.6980538   .1071496    -6.51   0.000    -.9080631   -.4880445
        Diff |   -2.21274    .308947    -7.16   0.000    -2.818265   -1.607215
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   1.895378   .1803061    10.51   0.000     1.541985    2.248772
        Diff |  -.3487671   .0559868    -6.23   0.000    -.4584993    -.239035
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   3.030103   .3708462     8.17   0.000     2.303258    3.756948
        Diff |  -.3700256   .0477066    -7.76   0.000    -.4635289   -.2765224
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |     1.4434   .1438785    10.03   0.000     1.161403    1.725396
        Diff |   .3602111   .0649149     5.55   0.000     .2329802     .487442
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_322b.
newvar2 will get the values of CC21_350g.
newvar3 will get the values of CC21_350f.
newvar4 will get the values of CC21_321a.
newvar5 will get the values of CC21_324b.
newvar6 will get the values of CC21_323c.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3580.7649  
Iteration 1:   log likelihood = -3572.9793  
Iteration 2:   log likelihood =   -3572.97  
Iteration 3:   log likelihood =   -3572.97  

Fitting full model:

Iteration 0:   log likelihood = -3289.4718  
Iteration 1:   log likelihood = -3201.2622  
Iteration 2:   log likelihood = -3188.3126  
Iteration 3:   log likelihood = -3187.8027  
Iteration 4:   log likelihood = -3187.8018  
Iteration 5:   log likelihood =  -3187.802  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3187.802
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.130252   .1267064     8.92   0.000     .8819122    1.378592
        Diff |  -.7605863   .0934962    -8.13   0.000    -.9438355   -.5773372
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   -1.75332   .2224422    -7.88   0.000    -2.189298   -1.317341
        Diff |   1.711398   .1329952    12.87   0.000     1.450732    1.972063
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -2.324996   .2589533    -8.98   0.000    -2.832535   -1.817457
        Diff |   .7405092   .0603477    12.27   0.000       .62223    .8587884
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |  -1.929112   .1936846    -9.96   0.000    -2.308727   -1.549497
        Diff |    .352437   .0555263     6.35   0.000     .2436074    .4612665
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |  -3.003206   .4015926    -7.48   0.000    -3.790313   -2.216099
        Diff |   .3796831    .047826     7.94   0.000     .2859459    .4734204
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   .6400723   .0925531     6.92   0.000     .4586715     .821473
        Diff |   .9118965   .1570906     5.80   0.000     .6040045    1.219788
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_355e.
newvar2 will get the values of CC21_321d.
newvar3 will get the values of CC21_324d.
newvar4 will get the values of CC21_350a.
newvar5 will get the values of CC21_321f.
newvar6 will get the values of CC21_322a.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3562.2668  
Iteration 1:   log likelihood = -3553.5321  
Iteration 2:   log likelihood = -3553.5209  
Iteration 3:   log likelihood = -3553.5209  

Fitting full model:

Iteration 0:   log likelihood = -3246.1199  
Iteration 1:   log likelihood = -3065.4819  
Iteration 2:   log likelihood = -3052.4003  
Iteration 3:   log likelihood = -3051.6155  
Iteration 4:   log likelihood = -3051.6172  
Iteration 5:   log likelihood = -3051.6175  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3051.6175
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   2.919569   .3314994     8.81   0.000     2.269842    3.569295
        Diff |  -.6176699   .0522687   -11.82   0.000    -.7201148   -.5152251
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   1.080257   .1210421     8.92   0.000     .8430192    1.317496
        Diff |   .7892983    .097304     8.11   0.000     .5985859    .9800107
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   1.658857   .1553529    10.68   0.000     1.354371    1.963343
        Diff |  -.4769736   .0621778    -7.67   0.000    -.5988399   -.3551074
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   3.172945   .3880586     8.18   0.000     2.412364    3.933526
        Diff |  -.3759552   .0467746    -8.04   0.000    -.4676317   -.2842786
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   1.215762   .1574775     7.72   0.000     .9071115    1.524412
        Diff |  -2.131905    .206572   -10.32   0.000    -2.536778   -1.727031
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   2.064822   .1969036    10.49   0.000     1.678898    2.450746
        Diff |  -.5380769   .0572177    -9.40   0.000    -.6502216   -.4259322
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_321c.
newvar2 will get the values of CC21_350c.
newvar3 will get the values of CC21_323b.
newvar4 will get the values of CC21_322a.
newvar5 will get the values of CC21_320e.
newvar6 will get the values of CC21_324d.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3812.2407  
Iteration 1:   log likelihood = -3809.0024  
Iteration 2:   log likelihood = -3809.0015  
Iteration 3:   log likelihood = -3809.0015  

Fitting full model:

Iteration 0:   log likelihood = -3583.5752  
Iteration 1:   log likelihood = -3547.2182  
Iteration 2:   log likelihood = -3542.1401  
Iteration 3:   log likelihood = -3541.9924  
Iteration 4:   log likelihood =  -3541.992  
Iteration 5:   log likelihood =  -3541.992  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3541.992
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.143026   .1292714     8.84   0.000     .8896585    1.396393
        Diff |   .0320509   .0699803     0.46   0.647    -.1051079    .1692097
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -2.057982   .2779256    -7.40   0.000    -2.602707   -1.513258
        Diff |   1.289067   .0958152    13.45   0.000     1.101273    1.476862
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |    .786928    .104594     7.52   0.000     .5819274    .9919285
        Diff |   .4991357   .1056845     4.72   0.000     .2919979    .7062736
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |  -2.240421   .2866073    -7.82   0.000    -2.802162   -1.678681
        Diff |   .5318928   .0566781     9.38   0.000     .4208058    .6429799
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   .8265648   .1061979     7.78   0.000     .6184207    1.034709
        Diff |   .0871598   .0883465     0.99   0.324    -.0859961    .2603157
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |  -1.485148   .1634739    -9.08   0.000    -1.805551   -1.164745
        Diff |   .5101581   .0679816     7.50   0.000     .3769167    .6433995
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_320b.
newvar2 will get the values of CC21_350i.
newvar3 will get the values of CC21_350a.
newvar4 will get the values of CC21_323a.
newvar5 will get the values of CC21_355e.
newvar6 will get the values of CC21_322c.

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -3838.655  
Iteration 1:   log likelihood = -3833.6868  
Iteration 2:   log likelihood = -3833.6846  
Iteration 3:   log likelihood = -3833.6846  

Fitting full model:

Iteration 0:   log likelihood = -3405.0962  
Iteration 1:   log likelihood = -3265.5619  
Iteration 2:   log likelihood = -3258.4219  
Iteration 3:   log likelihood = -3257.9659  
Iteration 4:   log likelihood = -3257.9716  
Iteration 5:   log likelihood = -3257.9721  
Iteration 6:   log likelihood = -3257.9721  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3257.9721
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   2.067328   .1907415    10.84   0.000     1.693482    2.441175
        Diff |    .221106   .0520347     4.25   0.000     .1191199    .3230921
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -1.243728   .1337752    -9.30   0.000    -1.505923   -.9815334
        Diff |   1.327597   .1183051    11.22   0.000     1.095723     1.55947
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -3.603806   .4718035    -7.64   0.000    -4.528523   -2.679088
        Diff |   .3804612   .0448776     8.48   0.000     .2925027    .4684196
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |  -1.982467   .1820956   -10.89   0.000    -2.339367   -1.625566
        Diff |   .3835652   .0547468     7.01   0.000     .2762633     .490867
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |  -2.479894    .247218   -10.03   0.000    -2.964432   -1.995356
        Diff |   .6571147   .0550502    11.94   0.000     .5492184     .765011
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   1.230829    .118516    10.39   0.000      .998542    1.463116
        Diff |   .2071763   .0678592     3.05   0.002     .0741747     .340178
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_323a.
newvar2 will get the values of CC21_324b.
newvar3 will get the values of CC21_320c.
newvar4 will get the values of CC21_350c.
newvar5 will get the values of CC21_350d.
newvar6 will get the values of CC21_323f.

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -3709.834  
Iteration 1:   log likelihood = -3704.2788  
Iteration 2:   log likelihood = -3704.2766  
Iteration 3:   log likelihood = -3704.2766  

Fitting full model:

Iteration 0:   log likelihood = -3335.5461  
Iteration 1:   log likelihood = -3105.1905  
Iteration 2:   log likelihood = -3089.2558  
Iteration 3:   log likelihood = -3087.1939  
Iteration 4:   log likelihood = -3087.3522  
Iteration 5:   log likelihood =  -3087.334  
Iteration 6:   log likelihood = -3087.3341  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3087.3341
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |    4.01732   .5000307     8.03   0.000     3.037278    4.997362
        Diff |  -.3257651    .043916    -7.42   0.000    -.4118389   -.2396914
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   2.068099    .184716    11.20   0.000     1.706062    2.430136
        Diff |  -.4346417   .0553769    -7.85   0.000    -.5431785    -.326105
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   1.149094   .1160394     9.90   0.000      .921661    1.376527
        Diff |   .3969068    .076337     5.20   0.000     .2472889    .5465246
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   1.478334   .1580706     9.35   0.000     1.168521    1.788146
        Diff |  -1.524281    .118922   -12.82   0.000    -1.757364   -1.291199
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   1.317919   .1276953    10.32   0.000     1.067641    1.568197
        Diff |  -.9299129   .0877712   -10.59   0.000    -1.101941   -.7578846
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   4.746539   .7469178     6.35   0.000     3.282607    6.210471
        Diff |  -.3897247   .0427981    -9.11   0.000    -.4736075   -.3058419
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_324c.
newvar2 will get the values of CC21_321d.
newvar3 will get the values of CC21_323d.
newvar4 will get the values of CC21_350h.
newvar5 will get the values of CC21_322b.
newvar6 will get the values of CC21_350c.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3742.8949  
Iteration 1:   log likelihood = -3737.6538  
Iteration 2:   log likelihood = -3737.6521  
Iteration 3:   log likelihood = -3737.6521  

Fitting full model:

Iteration 0:   log likelihood = -3497.8785  
Iteration 1:   log likelihood = -3368.9465  
Iteration 2:   log likelihood = -3362.2559  
Iteration 3:   log likelihood = -3361.3134  
Iteration 4:   log likelihood = -3361.2835  
Iteration 5:   log likelihood = -3361.2833  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3361.2833
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   3.660684   .6275828     5.83   0.000     2.430645    4.890724
        Diff |  -.3037311   .0457205    -6.64   0.000    -.3933415   -.2141206
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   1.248601   .1330154     9.39   0.000     .9878953    1.509306
        Diff |   .7223977   .0843939     8.56   0.000     .5569888    .8878066
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -1.370464    .139575    -9.82   0.000    -1.644026   -1.096902
        Diff |   .2256738   .0644969     3.50   0.000     .0992621    .3520854
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   .9841664   .1095249     8.99   0.000     .7695015    1.198831
        Diff |  -.6084913   .0928949    -6.55   0.000     -.790562   -.4264205
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |  -1.648861   .1753888    -9.40   0.000    -1.992616   -1.305105
        Diff |   .6155068   .0681248     9.03   0.000     .4819846     .749029
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   1.743858   .2062818     8.45   0.000     1.339553    2.148163
        Diff |  -1.384217   .1060956   -13.05   0.000    -1.592161   -1.176274
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_323f.
newvar2 will get the values of CC21_322c.
newvar3 will get the values of CC21_350b.
newvar4 will get the values of CC21_350f.
newvar5 will get the values of CC21_320e.
newvar6 will get the values of CC21_321e.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3837.6666  
Iteration 1:   log likelihood = -3832.9316  
Iteration 2:   log likelihood = -3832.9289  
Iteration 3:   log likelihood = -3832.9289  

Fitting full model:

Iteration 0:   log likelihood = -3482.1376  
Iteration 1:   log likelihood = -3386.7842  
Iteration 2:   log likelihood = -3379.5305  
Iteration 3:   log likelihood = -3379.1451  
Iteration 4:   log likelihood = -3379.1455  
Iteration 5:   log likelihood = -3379.1456  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3379.1456
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.736162   .1668392    10.41   0.000     1.409163     2.06316
        Diff |   -.499557   .0609011    -8.20   0.000     -.618921   -.3801931
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -1.157769    .118531    -9.77   0.000    -1.390085    -.925452
        Diff |  -.2136361   .0708161    -3.02   0.003    -.3524331    -.074839
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   3.003346    .377693     7.95   0.000     2.263081     3.74361
        Diff |  -.3202176   .0467459    -6.85   0.000    -.4118378   -.2285973
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   2.518405   .2767268     9.10   0.000     1.976031     3.06078
        Diff |   -.724186   .0573109   -12.64   0.000    -.8365133   -.6118587
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |  -.9018466   .1017095    -8.87   0.000    -1.101194   -.7024997
        Diff |  -.0840841   .0826488    -1.02   0.309    -.2460727    .0779046
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   1.740951   .1798486     9.68   0.000     1.388455    2.093448
        Diff |  -1.062476   .0820224   -12.95   0.000    -1.223237   -.9017146
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_350e.
newvar2 will get the values of CC21_355d.
newvar3 will get the values of CC21_355e.
newvar4 will get the values of CC21_355b.
newvar5 will get the values of CC21_350i.
newvar6 will get the values of CC21_321a.

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -3701.627  
Iteration 1:   log likelihood = -3694.2873  
Iteration 2:   log likelihood =  -3694.283  
Iteration 3:   log likelihood =  -3694.283  

Fitting full model:

Iteration 0:   log likelihood = -3214.5458  
Iteration 1:   log likelihood = -2950.6413  
Iteration 2:   log likelihood = -2936.4529  
Iteration 3:   log likelihood =  -2934.044  
Iteration 4:   log likelihood =  -2934.126  
Iteration 5:   log likelihood = -2934.1469  
Iteration 6:   log likelihood = -2934.1474  
Iteration 7:   log likelihood = -2934.1472  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -2934.1472
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   2.528062   .2322859    10.88   0.000      2.07279    2.983334
        Diff |  -.7393163   .0553213   -13.36   0.000    -.8477441   -.6308885
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   2.381447   .2098389    11.35   0.000      1.97017    2.792723
        Diff |   -.439027   .0514373    -8.54   0.000    -.5398423   -.3382117
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   2.810468   .2670313    10.52   0.000     2.287097     3.33384
        Diff |  -.6490426   .0514877   -12.61   0.000    -.7499567   -.5481285
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   5.793651   1.123945     5.15   0.000     3.590759    7.996543
        Diff |  -.4594302   .0405286   -11.34   0.000    -.5388648   -.3799955
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |    1.22575   .1268133     9.67   0.000     .9772001    1.474299
        Diff |  -1.357469   .1173155   -11.57   0.000    -1.587403   -1.127534
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   1.851252   .1592173    11.63   0.000     1.539192    2.163312
        Diff |  -.3762523   .0564369    -6.67   0.000    -.4868665    -.265638
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_322c.
newvar2 will get the values of CC21_355a.
newvar3 will get the values of CC21_350i.
newvar4 will get the values of CC21_321d.
newvar5 will get the values of CC21_355e.
newvar6 will get the values of CC21_355d.

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -3776.713  
Iteration 1:   log likelihood = -3770.2351  
Iteration 2:   log likelihood = -3770.2319  
Iteration 3:   log likelihood = -3770.2319  

Fitting full model:

Iteration 0:   log likelihood = -3276.6671  
Iteration 1:   log likelihood = -3203.5777  
Iteration 2:   log likelihood = -3190.2284  
Iteration 3:   log likelihood = -3187.9451  
Iteration 4:   log likelihood = -3189.9905  
Iteration 5:   log likelihood = -3188.0137  
Iteration 6:   log likelihood = -3195.8379  (not concave)
Iteration 7:   log likelihood = -3193.9348  (not concave)
Iteration 8:   log likelihood = -3194.0581  (not concave)
Iteration 9:   log likelihood = -3192.6666  
Iteration 10:  log likelihood = -3188.8133  
Iteration 11:  log likelihood = -3189.0557  
Iteration 12:  log likelihood = -3188.3131  
Iteration 13:  log likelihood = -3192.7486  
Iteration 14:  log likelihood = -3188.6975  
Iteration 15:  log likelihood = -3188.8994  
Iteration 16:  log likelihood = -3188.6003  
Iteration 17:  log likelihood = -3189.9385  
Iteration 18:  log likelihood = -3188.2997  
Iteration 19:  log likelihood = -3195.1127  (not concave)
Iteration 20:  log likelihood = -3192.7405  (not concave)
Iteration 21:  log likelihood = -3192.5938  
Iteration 22:  log likelihood = -3188.5767  
Iteration 23:  log likelihood =  -3189.471  
Iteration 24:  log likelihood = -3187.8837  
Iteration 25:  log likelihood = -3190.9954  
Iteration 26:  log likelihood = -3188.5918  
Iteration 27:  log likelihood = -3189.0506  
Iteration 28:  log likelihood =  -3188.168  
Iteration 29:  log likelihood = -3192.8317  
Iteration 30:  log likelihood = -3188.8032  
Iteration 31:  log likelihood =  -3188.812  
Iteration 32:  log likelihood = -3188.8072  
Iteration 33:  log likelihood = -3188.8452  
Iteration 34:  log likelihood = -3188.7523  
Iteration 35:  log likelihood = -3189.0031  
Iteration 36:  log likelihood = -3188.3289  
Iteration 37:  log likelihood =  -3192.468  
Iteration 38:  log likelihood = -3188.5941  
Iteration 39:  log likelihood = -3188.9762  
Iteration 40:  log likelihood =  -3188.396  
Iteration 41:  log likelihood = -3192.2005  
Iteration 42:  log likelihood = -3188.4704  
Iteration 43:  log likelihood = -3189.2189  
Iteration 44:  log likelihood = -3188.2043  
Iteration 45:  log likelihood = -3193.2803  
Iteration 46:  log likelihood = -3189.1006  
Iteration 47:  log likelihood = -3188.6259  
Iteration 48:  log likelihood = -3189.6485  
Iteration 49:  log likelihood = -3187.8757  
Iteration 50:  log likelihood = -3195.8291  (not concave)
Iteration 51:  log likelihood = -3194.7237  (not concave)
Iteration 52:  log likelihood = -3194.0782  (not concave)
Iteration 53:  log likelihood = -3192.6826  
Iteration 54:  log likelihood = -3188.8798  
Iteration 55:  log likelihood = -3189.0804  
Iteration 56:  log likelihood = -3188.3101  
Iteration 57:  log likelihood = -3192.7892  
Iteration 58:  log likelihood = -3188.7166  
Iteration 59:  log likelihood = -3188.8894  
Iteration 60:  log likelihood =  -3188.627  
Iteration 61:  log likelihood = -3189.8188  
Iteration 62:  log likelihood = -3188.3656  
Iteration 63:  log likelihood = -3194.8094  (not concave)
Iteration 64:  log likelihood = -3193.2837  
Iteration 65:  log likelihood = -3190.2922  (backed up)
Iteration 66:  log likelihood = -3188.1696  
Iteration 67:  log likelihood = -3190.7084  
Iteration 68:  log likelihood = -3188.5819  
Iteration 69:  log likelihood = -3189.2364  
Iteration 70:  log likelihood = -3188.1871  
Iteration 71:  log likelihood = -3193.2674  
Iteration 72:  log likelihood = -3189.0275  
Iteration 73:  log likelihood = -3188.6922  
Iteration 74:  log likelihood = -3189.1832  
Iteration 75:  log likelihood = -3188.3942  
Iteration 76:  log likelihood = -3192.1367  
Iteration 77:  log likelihood = -3188.4119  
Iteration 78:  log likelihood = -3189.2392  
Iteration 79:  log likelihood = -3188.1853  
Iteration 80:  log likelihood = -3193.2465  
Iteration 81:  log likelihood = -3189.0784  
Iteration 82:  log likelihood =   -3188.64  
Iteration 83:  log likelihood =   -3189.31  
Iteration 84:  log likelihood = -3188.2085  
Iteration 85:  log likelihood = -3192.7024  
Iteration 86:  log likelihood = -3188.6698  
Iteration 87:  log likelihood = -3188.9308  
Iteration 88:  log likelihood =  -3188.516  
Iteration 89:  log likelihood = -3191.4747  
Iteration 90:  log likelihood = -3188.2086  
Iteration 91:  log likelihood = -3194.0761  (not concave)
Iteration 92:  log likelihood = -3193.8077  (not concave)
Iteration 93:  log likelihood = -3193.2225  
Iteration 94:  log likelihood = -3189.5296  (backed up)
Iteration 95:  log likelihood = -3188.3235  
Iteration 96:  log likelihood = -3190.4889  
Iteration 97:  log likelihood = -3188.3491  
Iteration 98:  log likelihood = -3197.8524  (not concave)
Iteration 99:  log likelihood =  -3200.649  (not concave)
Iteration 100: log likelihood =  -3196.509  (not concave)
Iteration 101: log likelihood = -3198.0303  (not concave)
Iteration 102: log likelihood = -3195.9327  (not concave)
Iteration 103: log likelihood = -3195.4196  (not concave)
Iteration 104: log likelihood = -3193.4142  
Iteration 105: log likelihood = -3191.5034  (backed up)
Iteration 106: log likelihood = -3188.1143  
Iteration 107: log likelihood = -3190.3479  
Iteration 108: log likelihood = -3188.1195  
Iteration 109: log likelihood = -3193.0839  (not concave)
Iteration 110: log likelihood = -3192.9366  
Iteration 111: log likelihood = -3190.6203  
Iteration 112: log likelihood = -3188.5819  
Iteration 113: log likelihood = -3197.9689  (not concave)
Iteration 114: log likelihood = -3195.9625  (not concave)
Iteration 115: log likelihood = -3193.7626  (not concave)
Iteration 116: log likelihood = -3191.8645  
Iteration 117: log likelihood = -3188.1379  
Iteration 118: log likelihood = -3190.0002  
Iteration 119: log likelihood = -3187.9709  
Iteration 120: log likelihood =  -3192.521  
Iteration 121: log likelihood =  -3189.026  
Iteration 122: log likelihood = -3188.7149  
Iteration 123: log likelihood = -3189.0159  
Iteration 124: log likelihood = -3188.2925  
Iteration 125: log likelihood = -3192.5835  
Iteration 126: log likelihood = -3188.6547  
Iteration 127: log likelihood = -3188.9462  
Iteration 128: log likelihood = -3188.4742  
Iteration 129: log likelihood =  -3191.758  
Iteration 130: log likelihood = -3188.2959  
Iteration 131: log likelihood = -3189.8287  
Iteration 132: log likelihood = -3188.1202  
Iteration 133: log likelihood = -3191.6572  
Iteration 134: log likelihood = -3188.7371  
Iteration 135: log likelihood = -3188.8989  
Iteration 136: log likelihood = -3188.5987  
Iteration 137: log likelihood = -3189.9408  
Iteration 138: log likelihood = -3188.2973  
Iteration 139: log likelihood = -3195.1388  (not concave)
Iteration 140: log likelihood = -3192.7839  (not concave)
Iteration 141: log likelihood = -3192.6065  
Iteration 142: log likelihood = -3188.5383  
Iteration 143: log likelihood = -3189.5465  
Iteration 144: log likelihood = -3187.8165  
Iteration 145: log likelihood = -3193.0628  
Iteration 146: log likelihood = -3189.0522  
Iteration 147: log likelihood = -3188.7573  
Iteration 148: log likelihood = -3188.9279  
Iteration 149: log likelihood = -3188.5343  
Iteration 150: log likelihood = -3191.3127  
Iteration 151: log likelihood = -3188.1688  
Iteration 152: log likelihood = -3194.2324  (not concave)
Iteration 153: log likelihood = -3194.0388  (not concave)
Iteration 154: log likelihood = -3193.2203  
Iteration 155: log likelihood = -3189.6116  
Iteration 156: log likelihood = -3188.3581  
Iteration 157: log likelihood = -3191.1399  
Iteration 158: log likelihood = -3188.3755  
Iteration 159: log likelihood =  -3189.834  
Iteration 160: log likelihood = -3188.3105  
Iteration 161: log likelihood = -3191.6101  
Iteration 162: log likelihood = -3188.5774  
Iteration 163: log likelihood = -3188.9987  
Iteration 164: log likelihood = -3188.3259  
Iteration 165: log likelihood = -3192.4849  
Iteration 166: log likelihood = -3188.6049  
Iteration 167: log likelihood = -3188.9715  
Iteration 168: log likelihood =  -3188.408  
Iteration 169: log likelihood = -3192.1404  
Iteration 170: log likelihood = -3188.4441  
Iteration 171: log likelihood = -3189.2317  
Iteration 172: log likelihood = -3188.1889  
Iteration 173: log likelihood = -3193.2751  
Iteration 174: log likelihood = -3189.0955  
Iteration 175: log likelihood = -3188.6292  
Iteration 176: log likelihood = -3189.6363  
Iteration 177: log likelihood = -3187.8775  
Iteration 178: log likelihood = -3195.9565  (not concave)
Iteration 179: log likelihood = -3194.7046  (not concave)
Iteration 180: log likelihood = -3194.5739  (not concave)
Iteration 181: log likelihood = -3192.9543  
Iteration 182: log likelihood = -3189.5968  
Iteration 183: log likelihood = -3188.1337  
Iteration 184: log likelihood = -3192.5999  
Iteration 185: log likelihood = -3188.5224  
Iteration 186: log likelihood = -3188.8408  
Iteration 187: log likelihood = -3191.9877  
Iteration 188: log likelihood = -3188.4569  
Iteration 189: log likelihood = -3189.2511  
Iteration 190: log likelihood = -3188.1596  
Iteration 191: log likelihood =  -3193.271  
Iteration 192: log likelihood = -3189.1062  
Iteration 193: log likelihood = -3188.6195  
Iteration 194: log likelihood = -3189.6744  
Iteration 195: log likelihood = -3187.8758  
Iteration 196: log likelihood = -3195.5329  (not concave)
Iteration 197: log likelihood = -3194.4426  (not concave)
Iteration 198: log likelihood = -3194.1592  (not concave)
Iteration 199: log likelihood = -3192.7253  (not concave)
Iteration 200: log likelihood =  -3192.285  
Iteration 201: log likelihood = -3188.2228  
Iteration 202: log likelihood = -3190.9888  (not concave)
Iteration 203: log likelihood = -3190.8825  
Iteration 204: log likelihood =  -3188.124  
Iteration 205: log likelihood = -3191.4418  
Iteration 206: log likelihood = -3188.5207  
Iteration 207: log likelihood = -3189.2358  
Iteration 208: log likelihood = -3188.8838  
Iteration 209: log likelihood = -3188.6264  
Iteration 210: log likelihood = -3189.8038  
Iteration 211: log likelihood = -3188.3714  
Iteration 212: log likelihood = -3194.8518  (not concave)
Iteration 213: log likelihood = -3193.3706  
Iteration 214: log likelihood = -3190.0774  (backed up)
Iteration 215: log likelihood = -3188.3177  
Iteration 216: log likelihood = -3190.7591  
Iteration 217: log likelihood = -3188.4215  
Iteration 218: log likelihood = -3189.8788  
Iteration 219: log likelihood = -3187.8357  
Iteration 220: log likelihood =  -3192.166  
Iteration 221: log likelihood = -3188.7812  
Iteration 222: log likelihood = -3188.8701  
Iteration 223: log likelihood =  -3188.685  
Iteration 224: log likelihood = -3189.2874  
Iteration 225: log likelihood = -3188.2495  
Iteration 226: log likelihood = -3192.5473  
Iteration 227: log likelihood = -3188.5831  
Iteration 228: log likelihood = -3188.9776  
Iteration 229: log likelihood = -3188.3951  
Iteration 230: log likelihood = -3192.2067  
Iteration 231: log likelihood = -3188.4734  
Iteration 232: log likelihood = -3189.2172  
Iteration 233: log likelihood = -3188.2066  
Iteration 234: log likelihood = -3193.2803  
Iteration 235: log likelihood = -3189.1007  
Iteration 236: log likelihood = -3188.6259  
Iteration 237: log likelihood = -3189.6486  
Iteration 238: log likelihood = -3187.8757  
Iteration 239: log likelihood = -3195.8287  (not concave)
Iteration 240: log likelihood = -3194.7235  (not concave)
Iteration 241: log likelihood = -3194.0778  (not concave)
Iteration 242: log likelihood = -3192.6822  
Iteration 243: log likelihood = -3188.8788  
Iteration 244: log likelihood =   -3189.08  
Iteration 245: log likelihood = -3188.3113  
Iteration 246: log likelihood = -3192.7839  
Iteration 247: log likelihood =  -3188.714  
Iteration 248: log likelihood = -3188.8906  
Iteration 249: log likelihood = -3188.6238  
Iteration 250: log likelihood = -3189.8336  
Iteration 251: log likelihood = -3188.3569  
Iteration 252: log likelihood = -3194.8601  (not concave)
Iteration 253: log likelihood = -3193.3325  
Iteration 254: log likelihood = -3190.2251  (backed up)
Iteration 255: log likelihood = -3188.3627  
Iteration 256: log likelihood = -3190.5544  
Iteration 257: log likelihood = -3188.5009  
Iteration 258: log likelihood = -3189.7764  
Iteration 259: log likelihood = -3187.8695  
Iteration 260: log likelihood = -3192.8936  
Iteration 261: log likelihood = -3189.0766  
Iteration 262: log likelihood = -3188.6719  
Iteration 263: log likelihood =  -3189.255  
Iteration 264: log likelihood = -3188.2955  
Iteration 265: log likelihood = -3192.4751  
Iteration 266: log likelihood = -3188.5597  
Iteration 267: log likelihood = -3189.1664  
Iteration 268: log likelihood = -3188.2851  
Iteration 269: log likelihood = -3191.0453  
Iteration 270: log likelihood = -3188.3577  
Iteration 271: log likelihood =  -3189.893  
Iteration 272: log likelihood = -3188.3549  
Iteration 273: log likelihood = -3191.3829  
Iteration 274: log likelihood = -3188.4686  
Iteration 275: log likelihood = -3189.2519  
Iteration 276: log likelihood = -3188.1551  
Iteration 277: log likelihood = -3193.3217  
Iteration 278: log likelihood = -3189.1065  
Iteration 279: log likelihood = -3188.6316  
Iteration 280: log likelihood = -3189.6341  
Iteration 281: log likelihood = -3187.8792  
Iteration 282: log likelihood = -3195.9275  (not concave)
Iteration 283: log likelihood = -3194.6762  (not concave)
Iteration 284: log likelihood =  -3194.503  (not concave)
Iteration 285: log likelihood = -3192.9213  
Iteration 286: log likelihood = -3189.3853  
Iteration 287: log likelihood = -3188.6407  
Iteration 288: log likelihood = -3190.5119  
Iteration 289: log likelihood = -3187.9799  
Iteration 290: log likelihood = -3195.4825  (not concave)
Iteration 291: log likelihood = -3194.1278  
Iteration 292: log likelihood = -3190.6955  (backed up)
Iteration 293: log likelihood = -3188.2952  
Iteration 294: log likelihood = -3191.1919  
Iteration 295: log likelihood = -3188.3578  
Iteration 296: log likelihood = -3189.8367  
Iteration 297: log likelihood = -3188.2379  
Iteration 298: log likelihood = -3191.7026  
Iteration 299: log likelihood = -3188.6694  
Iteration 300: log likelihood = -3188.9508  
convergence not achieved

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3188.9508
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.048309   .1064683     9.85   0.000     .8396352    1.256983
        Diff |   .2499782   .0816114     3.06   0.002     .0900228    .4099336
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -7.560355   3.565853    -2.12   0.034     -14.5493   -.5714125
        Diff |   .4096913   .0478767     8.56   0.000     .3158546     .503528
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -1.127824   .1235761    -9.13   0.000    -1.370029   -.8856195
        Diff |   1.438272   .1342807    10.71   0.000     1.175087    1.701458
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |  -1.065418   .1154713    -9.23   0.000    -1.291737   -.8390979
        Diff |  -.7750439   .1003434    -7.72   0.000    -.9717134   -.5783744
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   -2.70438   .2826422    -9.57   0.000    -3.258349   -2.150412
        Diff |   .6673165   .0641099    10.41   0.000     .5416634    .7929697
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |  -2.651086   .2961677    -8.95   0.000    -3.231564   -2.070608
        Diff |   .4370586   .0619614     7.05   0.000     .3156164    .5585008
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_324a.
newvar2 will get the values of CC21_321f.
newvar3 will get the values of CC21_320c.
newvar4 will get the values of CC21_321a.
newvar5 will get the values of CC21_323d.
newvar6 will get the values of CC21_322e.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3631.4171  
Iteration 1:   log likelihood = -3624.8443  
Iteration 2:   log likelihood = -3624.8342  
Iteration 3:   log likelihood = -3624.8342  

Fitting full model:

Iteration 0:   log likelihood = -3336.6645  
Iteration 1:   log likelihood = -3164.2291  
Iteration 2:   log likelihood = -3151.3817  
Iteration 3:   log likelihood = -3150.2658  
Iteration 4:   log likelihood = -3150.2439  
Iteration 5:   log likelihood = -3150.2442  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3150.2442
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   3.588488    .516718     6.94   0.000     2.575739    4.601236
        Diff |   -.435997   .0466408    -9.35   0.000    -.5274114   -.3445827
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   1.530831   .1920861     7.97   0.000     1.154349    1.907313
        Diff |   -1.85379    .154595   -11.99   0.000    -2.156791    -1.55079
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   1.514829   .1493616    10.14   0.000     1.222086    1.807572
        Diff |   .3511299   .0630044     5.57   0.000     .2276436    .4746162
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   2.218858   .2228882     9.96   0.000     1.782005    2.655711
        Diff |  -.3303122   .0521237    -6.34   0.000    -.4324728   -.2281516
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |  -1.023837   .1092244    -9.37   0.000    -1.237913   -.8097616
        Diff |   .2705682   .0783929     3.45   0.001     .1169208    .4242155
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   1.934114   .1886252    10.25   0.000     1.564415    2.303812
        Diff |  -.7761875   .0655683   -11.84   0.000     -.904699   -.6476761
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_355b.
newvar2 will get the values of CC21_322e.
newvar3 will get the values of CC21_324c.
newvar4 will get the values of CC21_355a.
newvar5 will get the values of CC21_350c.
newvar6 will get the values of CC21_350d.

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -3610.647  
Iteration 1:   log likelihood = -3603.8925  
Iteration 2:   log likelihood = -3603.8885  
Iteration 3:   log likelihood = -3603.8885  

Fitting full model:

Iteration 0:   log likelihood = -3107.3798  
Iteration 1:   log likelihood = -2758.7598  
Iteration 2:   log likelihood = -2728.9828  
Iteration 3:   log likelihood =   -2727.78  
Iteration 4:   log likelihood = -2727.3391  
Iteration 5:   log likelihood = -2727.5214  
Iteration 6:   log likelihood = -2727.4553  
Iteration 7:   log likelihood = -2727.4804  
Iteration 8:   log likelihood = -2727.4709  
Iteration 9:   log likelihood = -2727.4745  
Iteration 10:  log likelihood = -2727.4731  
Iteration 11:  log likelihood = -2727.4736  
Iteration 12:  log likelihood = -2727.4734  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -2727.4734
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   5.795669   1.005075     5.77   0.000     3.825758     7.76558
        Diff |  -.4874084   .0442455   -11.02   0.000     -.574128   -.4006889
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   2.208079   .1915833    11.53   0.000     1.832583    2.583575
        Diff |  -.7810074   .0615191   -12.70   0.000    -.9015826   -.6604322
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   2.927911   .2561602    11.43   0.000     2.425846    3.429976
        Diff |  -.3653467   .0526339    -6.94   0.000    -.4685072   -.2621861
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   4.925248    .664837     7.41   0.000     3.622191    6.228304
        Diff |  -.4302282   .0461217    -9.33   0.000    -.5206251   -.3398312
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   2.091825   .2061807    10.15   0.000     1.687718    2.495932
        Diff |  -1.327309     .08288   -16.01   0.000     -1.48975   -1.164867
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   1.341416    .124364    10.79   0.000     1.097667    1.585165
        Diff |  -.9492475   .0865313   -10.97   0.000    -1.118846   -.7796493
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_322a.
newvar2 will get the values of CC21_321c.
newvar3 will get the values of CC21_320b.
newvar4 will get the values of CC21_321d.
newvar5 will get the values of CC21_324a.
newvar6 will get the values of CC21_320e.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3999.5918  
Iteration 1:   log likelihood = -3994.8506  
Iteration 2:   log likelihood = -3994.8487  
Iteration 3:   log likelihood = -3994.8487  

Fitting full model:

Iteration 0:   log likelihood = -3669.4373  
Iteration 1:   log likelihood = -3568.9256  
Iteration 2:   log likelihood = -3566.0708  
Iteration 3:   log likelihood = -3565.9757  
Iteration 4:   log likelihood = -3565.9754  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3565.9754
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.761771    .175099    10.06   0.000     1.418583    2.104959
        Diff |  -.5701043    .063648    -8.96   0.000     -.694852   -.4453565
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -1.673509   .1765388    -9.48   0.000    -2.019518   -1.327499
        Diff |  -.0186571   .0563573    -0.33   0.741    -.1291153    .0918012
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -1.673272   .1694266    -9.88   0.000    -2.005342   -1.341202
        Diff |  -.2260648   .0579527    -3.90   0.000    -.3396501   -.1124795
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   1.471891   .1660136     8.87   0.000     1.146511    1.797272
        Diff |   .6576157   .0750951     8.76   0.000     .5104319    .8047995
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   2.738877   .3474364     7.88   0.000     2.057915     3.41984
        Diff |  -.4527332   .0516021    -8.77   0.000    -.5538715    -.351595
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |  -.8281725   .0989792    -8.37   0.000    -1.022168   -.6341769
        Diff |  -.0822349   .0882129    -0.93   0.351     -.255129    .0906593
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_323e.
newvar2 will get the values of CC21_324a.
newvar3 will get the values of CC21_350i.
newvar4 will get the values of CC21_321d.
newvar5 will get the values of CC21_322e.
newvar6 will get the values of CC21_350d.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3503.8432  
Iteration 1:   log likelihood =  -3496.689  
Iteration 2:   log likelihood = -3496.6842  
Iteration 3:   log likelihood = -3496.6842  

Fitting full model:

Iteration 0:   log likelihood = -3218.5252  
Iteration 1:   log likelihood = -3171.4062  
Iteration 2:   log likelihood = -3158.2481  
Iteration 3:   log likelihood = -3156.9625  
Iteration 4:   log likelihood = -3156.9007  
Iteration 5:   log likelihood = -3156.9069  
Iteration 6:   log likelihood = -3156.8993  
Iteration 7:   log likelihood = -3156.9028  
Iteration 8:   log likelihood = -3156.9012  
Iteration 9:   log likelihood = -3156.9019  
Iteration 10:  log likelihood = -3156.9016  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3156.9016
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |    .542117   .1042095     5.20   0.000     .3378701     .746364
        Diff |   2.764782   .4962096     5.57   0.000     1.792229    3.737335
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -4.601369   1.122718    -4.10   0.000    -6.801855   -2.400883
        Diff |   .4414054   .0435405    10.14   0.000     .3560675    .5267433
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -1.455058    .157402    -9.24   0.000    -1.763561   -1.146556
        Diff |   1.223374    .103313    11.84   0.000     1.020884    1.425863
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |  -.9553488   .1166001    -8.19   0.000    -1.183881   -.7268168
        Diff |  -.8497672   .1132062    -7.51   0.000    -1.071647   -.6278872
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |  -1.906026   .2012059    -9.47   0.000    -2.300382    -1.51167
        Diff |   .7959389   .0699507    11.38   0.000      .658838    .9330398
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |  -1.465224   .1520177    -9.64   0.000    -1.763173   -1.167274
        Diff |   .8779162   .0838334    10.47   0.000     .7136057    1.042227
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_324a.
newvar2 will get the values of CC21_321d.
newvar3 will get the values of CC21_323d.
newvar4 will get the values of CC21_320e.
newvar5 will get the values of CC21_322e.
newvar6 will get the values of CC21_355b.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3903.8927  
Iteration 1:   log likelihood =  -3897.646  
Iteration 2:   log likelihood = -3897.6427  
Iteration 3:   log likelihood = -3897.6427  

Fitting full model:

Iteration 0:   log likelihood = -3588.4985  
Iteration 1:   log likelihood = -3382.0581  
Iteration 2:   log likelihood = -3358.5814  
Iteration 3:   log likelihood = -3354.9775  
Iteration 4:   log likelihood = -3355.1443  
Iteration 5:   log likelihood = -3355.1068  
Iteration 6:   log likelihood = -3355.0981  
Iteration 7:   log likelihood =  -3355.106  
Iteration 8:   log likelihood = -3355.1019  
Iteration 9:   log likelihood =  -3355.104  
Iteration 10:  log likelihood =  -3355.103  
Iteration 11:  log likelihood = -3355.1035  
Iteration 12:  log likelihood = -3355.1032  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3355.1032
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   3.362009   .3762251     8.94   0.000     2.624621    4.099397
        Diff |  -.4551559    .050911    -8.94   0.000    -.5549397   -.3553722
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   1.080119   .1170866     9.22   0.000     .8506337    1.309605
        Diff |   .7811964   .0973856     8.02   0.000     .5903242    .9720686
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -1.175625   .1161598   -10.12   0.000    -1.403294   -.9479557
        Diff |   .2351464   .0727052     3.23   0.001     .0926468    .3776459
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |  -.7357511   .0908757    -8.10   0.000    -.9138642    -.557638
        Diff |  -.1043937   .0981186    -1.06   0.287    -.2967027    .0879152
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |    2.01397   .1855958    10.85   0.000     1.650209    2.377731
        Diff |  -.7753407   .0652053   -11.89   0.000    -.9031408   -.6475406
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   6.286123   1.559163     4.03   0.000      3.23022    9.342026
        Diff |  -.4489506   .0449429    -9.99   0.000    -.5370372   -.3608641
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_350b.
newvar2 will get the values of CC21_320c.
newvar3 will get the values of CC21_355c.
newvar4 will get the values of CC21_355b.
newvar5 will get the values of CC21_350c.
newvar6 will get the values of CC21_321f.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3408.2055  
Iteration 1:   log likelihood = -3401.7523  
Iteration 2:   log likelihood = -3401.7424  
Iteration 3:   log likelihood = -3401.7424  

Fitting full model:

Iteration 0:   log likelihood = -3093.2598  
Iteration 1:   log likelihood = -2948.4401  
Iteration 2:   log likelihood =  -2930.053  
Iteration 3:   log likelihood =  -2928.379  
Iteration 4:   log likelihood = -2928.3313  
Iteration 5:   log likelihood = -2928.3394  
Iteration 6:   log likelihood = -2928.3394  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -2928.3394
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   2.702026   .2993489     9.03   0.000     2.115313    3.288739
        Diff |  -.3260346    .048781    -6.68   0.000    -.4216436   -.2304256
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   1.397856   .1394008    10.03   0.000     1.124636    1.671077
        Diff |   .3599992    .066449     5.42   0.000     .2297616    .4902369
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   1.125413   .1167011     9.64   0.000     .8966829    1.354143
        Diff |  -.8815785   .0960615    -9.18   0.000    -1.069855   -.6933014
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   4.439413   .8483084     5.23   0.000     2.776759    6.102067
        Diff |  -.4561481   .0441229   -10.34   0.000    -.5426274   -.3696689
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   2.101505   .2273318     9.24   0.000     1.655943    2.547067
        Diff |  -1.286906   .0848246   -15.17   0.000    -1.453159   -1.120653
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |    1.16374   .1539238     7.56   0.000      .862055    1.465425
        Diff |  -2.198265   .2208545    -9.95   0.000    -2.631132   -1.765398
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_350g.
newvar2 will get the values of CC21_324a.
newvar3 will get the values of CC21_350a.
newvar4 will get the values of CC21_324c.
newvar5 will get the values of CC21_323f.
newvar6 will get the values of CC21_321e.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3525.8365  
Iteration 1:   log likelihood = -3518.6552  
Iteration 2:   log likelihood = -3518.6468  
Iteration 3:   log likelihood = -3518.6468  

Fitting full model:

Iteration 0:   log likelihood = -3168.4012  
Iteration 1:   log likelihood = -2814.0343  
Iteration 2:   log likelihood = -2783.9371  
Iteration 3:   log likelihood = -2779.1975  
Iteration 4:   log likelihood = -2779.2811  
Iteration 5:   log likelihood = -2779.2103  
Iteration 6:   log likelihood =  -2779.212  
Iteration 7:   log likelihood = -2779.2124  
Iteration 8:   log likelihood = -2779.2123  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -2779.2123
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.613621    .187263     8.62   0.000     1.246592     1.98065
        Diff |  -1.811607   .1372807   -13.20   0.000    -2.080672   -1.542541
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |   6.240487    1.47881     4.22   0.000     3.342071    9.138902
        Diff |  -.4438637   .0416081   -10.67   0.000     -.525414   -.3623133
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   2.726332   .2455573    11.10   0.000     2.245049    3.207615
        Diff |   -.428209   .0503258    -8.51   0.000    -.5268458   -.3295723
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   3.645357   .3801996     9.59   0.000      2.90018    4.390535
        Diff |  -.3426122    .046097    -7.43   0.000    -.4329607   -.2522637
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   1.770631   .1530019    11.57   0.000     1.470753    2.070509
        Diff |  -.5200264   .0609737    -8.53   0.000    -.6395327   -.4005201
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   1.378168   .1365834    10.09   0.000     1.110469    1.645866
        Diff |  -1.225513   .0998622   -12.27   0.000    -1.421239   -1.029787
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_321f.
newvar2 will get the values of CC21_321c.
newvar3 will get the values of CC21_321e.
newvar4 will get the values of CC21_350b.
newvar5 will get the values of CC21_323a.
newvar6 will get the values of CC21_324b.

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -3581.963  
Iteration 1:   log likelihood = -3576.3643  
Iteration 2:   log likelihood =  -3576.355  
Iteration 3:   log likelihood =  -3576.355  

Fitting full model:

Iteration 0:   log likelihood = -3352.0809  
Iteration 1:   log likelihood = -3146.7924  
Iteration 2:   log likelihood = -3130.3704  
Iteration 3:   log likelihood = -3129.2146  
Iteration 4:   log likelihood = -3129.2092  
Iteration 5:   log likelihood = -3129.2092  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3129.2092
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.175113   .1615274     7.28   0.000     .8585248      1.4917
        Diff |  -2.177959   .2250476    -9.68   0.000    -2.619045   -1.736874
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -.8784289   .1014308    -8.66   0.000     -1.07723   -.6796283
        Diff |  -.0365773   .0841484    -0.43   0.664    -.2015052    .1283505
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   1.704783    .177324     9.61   0.000     1.357234    2.052331
        Diff |  -1.074816   .0837197   -12.84   0.000    -1.238903   -.9107282
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |    2.59773   .2876472     9.03   0.000     2.033952    3.161508
        Diff |  -.3289653   .0492702    -6.68   0.000    -.4255332   -.2323975
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |    2.15502   .2138238    10.08   0.000     1.735933    2.574106
        Diff |  -.3688433   .0531608    -6.94   0.000    -.4730366   -.2646501
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |    2.44954   .2600805     9.42   0.000     1.939792    2.959289
        Diff |  -.4022254   .0511837    -7.86   0.000    -.5025436   -.3019073
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_350d.
newvar2 will get the values of CC21_323d.
newvar3 will get the values of CC21_324b.
newvar4 will get the values of CC21_321a.
newvar5 will get the values of CC21_320b.
newvar6 will get the values of CC21_350h.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3972.5445  
Iteration 1:   log likelihood = -3967.3324  
Iteration 2:   log likelihood = -3967.3305  
Iteration 3:   log likelihood = -3967.3305  

Fitting full model:

Iteration 0:   log likelihood = -3597.4206  
Iteration 1:   log likelihood = -3509.8527  
Iteration 2:   log likelihood =  -3506.422  
Iteration 3:   log likelihood = -3506.3769  
Iteration 4:   log likelihood = -3506.3771  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3506.3771
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.376346   .1402973     9.81   0.000     1.101369    1.651324
        Diff |  -.8892944   .0860719   -10.33   0.000    -1.057992   -.7205966
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |  -1.295466   .1306474    -9.92   0.000     -1.55153   -1.039402
        Diff |   .2327545   .0666342     3.49   0.000     .1021539    .3633552
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |   2.649198   .3049934     8.69   0.000     2.051422    3.246974
        Diff |  -.3817517   .0500791    -7.62   0.000    -.4799049   -.2835984
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   2.048965   .2043417    10.03   0.000     1.648463    2.449467
        Diff |  -.3362461   .0540476    -6.22   0.000    -.4421775   -.2303148
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |   -2.00815   .2025234    -9.92   0.000    -2.405088   -1.611211
        Diff |  -.2116731   .0529239    -4.00   0.000     -.315402   -.1079442
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |   1.141707    .117986     9.68   0.000      .910459    1.372956
        Diff |  -.5496758   .0808398    -6.80   0.000    -.7081189   -.3912328
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)
newvar1 will get the values of CC21_320b.
newvar2 will get the values of CC21_321d.
newvar3 will get the values of CC21_355a.
newvar4 will get the values of CC21_323e.
newvar5 will get the values of CC21_350h.
newvar6 will get the values of CC21_350i.

Fitting fixed-effects model:

Iteration 0:   log likelihood = -3663.4261  
Iteration 1:   log likelihood = -3658.1543  
Iteration 2:   log likelihood = -3658.1523  
Iteration 3:   log likelihood = -3658.1523  

Fitting full model:

Iteration 0:   log likelihood = -3401.7639  
Iteration 1:   log likelihood = -3324.4437  
Iteration 2:   log likelihood = -3316.9366  
Iteration 3:   log likelihood = -3315.5501  
Iteration 4:   log likelihood = -3315.9748  
Iteration 5:   log likelihood = -3315.6119  
Iteration 6:   log likelihood = -3315.8394  
Iteration 7:   log likelihood = -3315.6792  
Iteration 8:   log likelihood = -3315.7897  
Iteration 9:   log likelihood = -3315.7095  
Iteration 10:  log likelihood = -3315.7588  
Iteration 11:  log likelihood = -3315.7287  
Iteration 12:  log likelihood = -3315.7472  
Iteration 13:  log likelihood = -3315.7359  
Iteration 14:  log likelihood = -3315.7429  
Iteration 15:  log likelihood = -3315.7386  
Iteration 16:  log likelihood = -3315.7412  
Iteration 17:  log likelihood = -3315.7396  
Iteration 18:  log likelihood = -3315.7406  
Iteration 19:  log likelihood =   -3315.74  
Iteration 20:  log likelihood = -3315.7404  
Iteration 21:  log likelihood = -3315.7401  

Two-parameter logistic model                             Number of obs = 1,000
Log likelihood = -3315.7401
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
newvar1      |
     Discrim |   1.766366   .1776444     9.94   0.000      1.41819    2.114543
        Diff |   .2377143   .0584038     4.07   0.000     .1232449    .3521836
-------------+----------------------------------------------------------------
newvar2      |
     Discrim |    -.78313   .1057401    -7.41   0.000    -.9903769   -.5758831
        Diff |  -.9930416   .1457635    -6.81   0.000    -1.278733   -.7073503
-------------+----------------------------------------------------------------
newvar3      |
     Discrim |  -5.156048   1.341168    -3.84   0.000    -7.784689   -2.527408
        Diff |   .3941041   .0437846     9.00   0.000     .3082878    .4799204
-------------+----------------------------------------------------------------
newvar4      |
     Discrim |   .6722923   .1098831     6.12   0.000     .4569254    .8876593
        Diff |    2.29381   .3409327     6.73   0.000     1.625595    2.962026
-------------+----------------------------------------------------------------
newvar5      |
     Discrim |  -1.273901    .133359    -9.55   0.000     -1.53528   -1.012522
        Diff |   .5270906   .0764734     6.89   0.000     .3772056    .6769757
-------------+----------------------------------------------------------------
newvar6      |
     Discrim |  -1.421055   .1598901    -8.89   0.000    -1.734434   -1.107676
        Diff |   1.236703   .1082496    11.42   0.000     1.024537    1.448868
------------------------------------------------------------------------------
(option pr assumed)
(option conditional(ebmeans) assumed)
(using 7 quadrature points)

. 
. * Local scale correlations with random national policy scales 
. 
. local varlist localscale natscale1 natscale2 natscale3 natscale4 natscale5 natscale6 natscale7
>  natscale8 natscale9 natscale10 natscale11 natscale12 natscale13 natscale14 natscale15 natscal
> e16 natscale17 natscale18 natscale19 natscale20 natscale21 natscale22 natscale23 natscale24 na
> tscale25 natscale26 natscale27 natscale28 natscale29 natscale30

. 
. local nvars : word count `varlist' 

. 
. local N = `nvars' * (`nvars' - 1) / 2 

. 
. if `N' > _N set obs `N' 

. 
. gen x = "" 
(1,000 missing values generated)

. gen y = "" 
(1,000 missing values generated)

. gen r = . 
(1,000 missing values generated)

. local k = 1 

. tokenize "`varlist'" 

. 
. forval i = 1/`nvars' { 
  2.     local J = `i' + 1 
  3.     forval j = `J'/`nvars' { 
  4.         quietly {
  5.             corr ``i'' ``j'' 
  6.             replace x = "``i''" in `k' 
  7.             replace y = "``j''" in `k' 
  8.             replace r = r(rho) in `k' 
  9.         }
 10.         local ++k 
 11.     }
 12. }

. 
. * Make histogram showing distribution of correlations 
. replace r = abs(r)
(228 real changes made)

. 
. twoway histogram r if x!="localscale", color(black%30) percent || histogram r if x=="localscal
> e", color(navy%60) percent bin(10) legend(order(2 "National scale vs." "national scale" 1 "Loc
> al scale vs." "national scale") pos(6) row(1)) xtitle("Absolute value of correlation coefficie
> nts") xlabel(0 .2 .4 .6 .8 1) aspect(1)

. graph export FigureA4.png, replace
file /Users/bschaf03/Dropbox/Local and National Ideology Survey/Analysis/Replication
    Files/FigureA4.png saved as PNG format

. 
. log close
      name:  <unnamed>
       log:  /Users/bschaf03/Dropbox/Local and National Ideology Survey/Analysis/Replication Fil
> es/ces_replication.log
  log type:  text
 closed on:  20 Jun 2024, 12:16:49
------------------------------------------------------------------------------------------------
